Electricity Bill Research Articles

Sistem Kontrol Lampu Otomatis Pencegah Hama Berbasis Panel Surya dan Mikrokontroler

Solar panels are devices that can convert solar energy into electrical energy. Photovoltaic technology (photovoltaic / PV) is used to convert solar radiation into electrical energy. Apart from being environmentally friendly, one of the main advantages of using solar panels as an energy source is that it can save on electricity bills. With solar panels can save electricity costs. plants that require intensive handling that requires them to stay for an unspecified period of time. The author is working on this scientific work with the aim of being able to design practical, economical, and efficient lighting in garden homes by using renewable energy or PLTS. Shallot farmers are now back using pest control to produce high-quality, spicy-tasting and pest-free shallots that make shallots rot. The right solution is to make an automatic pest control system for onion pests based on microcontrollers and solar panels that can save farmers' electricity. Solar panels are devices that convert sunlight energy into electrical energy. The solar panel functions to charge the control system device. Another advantage of this control system tool is that it can prevent onion pests with 2 25-watt LED lights for 7 hours at night

Implementation of DC net metering scheme in off-board EV charging systems with a time of usage capability

Dynamic pricing is one of the most significant aspects of strengthening the existing power grid. The widespread adoption of electric vehicles (EVs) increases the average electricity demand and peak power consumption. Meanwhile, the uncoordinated EV charging makes the condition even more severe by widening the gap between power supply and demand. Consequently, it demands the reinforcement of the current electricity network. However, a net-metering policy based on time of usage (TOU) pricing helps in demand side management including vehicle-to-grid (V2G) scheduling. It reduces the impact of peak load and provides incentives for all EV owners and utility operators. In this research, a problem has been formulated to reduce the electricity bill with TOU-critical peak pricing (CPP)/peak time rebate (PTR) based tariff structure to coordinate the EV charging. The economic analysis of the flat and proposed pricing structure has been discussed with the help of case studies. Further, the DC net meter designed and developed by the authors has been tested on the proposed pricing scheme to minimize the losses and improvise the load curve.

Optimal energy management, technical, economic, social, political and environmental benefit analysis of a grid-connected PV/WT/FC hybrid energy system

The global energy consumption is increasing every day owing to the economic growth, urbanization, population growth, high standard of living and high income level. The conventional power plants cannot satisfy the world energy needs without some noticeable challenges such as greenhouse gas emissions, acid rain, health challenges, global warming, climate change and air pollution. The feasible alternative solution to meet ever increasing energy consumption in residential, commercial and industrial sectors of the economy is renewable energy sources (RESs) owing to the technical, political, societal, financial and environmental benefits. The hybrid energy system (HES) that is made up of wind turbine (WT), fuel cell (FC), photovoltaic (PV) and utility grid is proposed in the study to meet the energy needs of distinctive users in Cape Town, South Africa. The study's goal can be accomplished by making use of fmincon optimization technique to minimize the life cycle emission (LCE) of greenhouse gas (GHG), life cycle cost (LCC) and monetary value of energy purchased from the grid and maximize the job creation (JC), human development index (HDI), annual benefit (AB) and usage of PV, WT and FC. The effectiveness of the simulation technique applied in the paper can be assessed by comparing the HSE results with those from the base system. The outcomes of the HES offer a better performance with a payback period of 2.75 years, annual benefit of $329530, annual energy cost saving of $6899.3, annual electricity bill of $62747, potential energy saving of 52.37% and LCC of $419300. The annual energy purchased from the grid has reduced from 146,190 kWh to 59,593 kWh; this translates to 59.24% reduction when compared to the base system. The proposed HES with a non-renewable energy usage (NREU) of 40.78% and a renewable energy usage (REU) of 59.22% is financially feasible with a cost of energy (COE) of 0.0122 $/kWh and ecologically friendly with CO2, SO2 and NOx LCE reductions of 58.85%, 58.81% and 58.29% when compared with the base system. This indicates that PV, FC and WT can be used in the traditional power system to lower the annual electricity bill, energy purchased from the utility grid and GHG emissions. The results obtained from the assessment of the proposed HES serve as crucial decision-supporting tools that can be used by designers and operators in choosing the right parts for their power systems. The outcomes of the study can be utilized to enhance sustainability of electricity supply and minimize poverty in the developing countries.

The Economic Value of New Sustainable Products: The Case of Photovoltaic Thermal (PVT) Hybrid Solar Collectors

Among green energy technologies, solar power is a promising technology in the net-zero era of the power industry. Conventional solar modules have decreasing efficiency weaknesses as the temperature rises, whereas photovoltaic thermal (PVT) systems do not remove heat from the module or lower the temperature. However, the profitability of PVT systems has been underestimated. The profitability of new convergence products, such as PVT technology, entering the market for the first time allows for the evaluation of the appropriate starting price and policy requirements to support the product’s market entrance. We used the contingent valuation method (CVM) to solve this problem. The survey derived the respondents’ mean willingness to pay (WTP) and the total amount of social benefits. Among these CVM models, the one-and-one-half-bound (OOHB) spike model was used to distinguish zero WTP. Based on a survey of 300 households, respondents were willing to pay an additional yearly average income tax of KRW 10,608 (USD 7.90), and the total social benefit of PVT technology was calculated to be KRW 145 billion (USD 108 million). This result shows that individuals evaluate the value of PVT as 3.69% of their monthly electricity bill and 2.8% of their monthly gas bill. Several additional factors that influence WTP were analyzed, indicating a high possibility of PVT adoption by individuals who have replaced or are planning to replace photovoltaic (PV) modules. This study is significant because it examines the economic value of PVT and proposes a focus group for the effective market entry of new carbon neutrality products.

Smart power management strategy controlling domestic solar solutions in sub-Saharan countries

AbstractLimitations such as maximum power consumption during peak hours, scheduled load shedding and unplanned brownouts are problems of weak and stressed electricity grids. To compensate for such shortage of energy, the usage of renewable energies is a solution delivering increased reliability for consumers. Although sub-Saharan African countries suffer from unreliable grids, they benefit from large amounts of solar radiation throughout the year. Therefore, domestic solar systems including photovoltaic panels, battery storage and solar water heating are attractive solutions to supply affordable and reliable energy and hot water for consumers. However, to deliver the best management of the electrical loads in the face of unplanned brownouts, a smart power management strategy is required. Consequently, the proposed strategy relies on the predicted values of power generation and power consumption to autonomously control the system in both on-grid and off-grid modes. This method is evaluated using a case study relying on the measured electrical load and the hot water consumption data of a low-income house in Botswana. Results show that in addition to delivering sustainable support for the utility grid by decreasing the power consumption in peak hours, the proposed method reduces annual consumer electricity bill by 64% and increases the reliability of electricity supply from 95.5 to 99.5%. Thereby providing affordable and reliable solution to unreliable power supply due to stressed grids.

Mitigating anomalous electricity consumption in smart cities using an AI‐based stacked‐generalization technique

AbstractEnergy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non‐technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the main issue in maintaining grid stability and cause revenue loss to the utility. The automatic metering infrastructure (AMI) system has reduced grid instability but it has opened up new ways for NTLs in the form of different cyber‐physical theft attacks (CPTA). Machine learning (ML) techniques can be used to detect and minimize CPTA. However, they have certain limitations and cannot capture the energy consumption patterns (ECPs) of all the users, which decreases the performance of ML techniques in detecting malicious users. In this paper, we propose a novel ML‐based stacked generalization method for the cyber‐physical theft issue in the smart grid. The original data obtained from the grid is preprocessed to improve model training and processing. This includes NaN‐imputation, normalization, outliers' capping, support vector machine‐synthetic minority oversampling technique (SVM‐SMOTE) balancing, and principal component analysis (PCA) based data reduction techniques. The pre‐processed dataset is provided to the ML models light gradient boosting (LGB), extra trees (ET), extreme gradient boosting (XGBoost), and random forest (RF), to accurately capture all consumers' overall ECP. The predictions from these base models are fed to a meta‐classifier multi‐layer perceptron (MLP). The MLP combines the learning capability of all the base models and gives an improved final prediction. The proposed structure is implemented and verified on the publicly available real‐time large dataset of the State Grid Corporation of China (SGCC). The proposed model outperformed the individual base classifiers and the existing research in terms of CPTA detection with false positive rate (FPR), false negative rate (FNR), F1‐score, and accuracy values of 0.72%, 2.05%, 97.6%, and 97.69%, respectively.

Enhancing Electricity Theft Detection through K-Nearest Neighbors and Logistic Regression Algorithms with Synthetic Minority Oversampling Technique: A Case Study on State Electricity Company (PLN) Customer Data

Electricity theft has caused massive losses and damage to electricity utilities. The damage affects the electricity supply’s quality and increases the generation load. The losses happen not only for the electricity utilities but also affect the legitimate users who have to pay excessive electricity bills. That is why the method to detect electricity theft is indispensable. Recently, machine learning algorithms have been used to develop a model for detecting electricity theft. However, most algorithms have problems due to imbalanced data, overfitting issues, and lack of data. Therefore, this paper proposes a solution that implements the oversampling technique to address the problems and increase the developed model’s accuracy. It is used to perform oversampling on the imbalanced dataset. Our proposed method consists of a pre-processing step to remove empty values and extract several parameters. After that, the oversampling technique is performed on the result of the pre-processing step. The logistic regression model combined with the oversampling techniques shows the best performance results on the developed model of electricity theft detection based on the state electricity company customers. The experiment shows that the proposed method, logistic regression combined with the synthetic minority oversampling technique, shows superior performance in terms of the accuracy of the training data and data testing, precision, recall, and F1-scores of 98.97%, 98.7%, 95%, 99%, and 97%, respectively. Moreover, the experiment also shows that the proposed solution outperforms existing methods.

Analysis of the energy performance strategies in a historical building used as a music school

Energy consumption in public education buildings depends on use and occupancy. To improve energy performance, energy audits are essential to identify specific solutions for each building. In this study, we conducted an energy audit of a historical building used as a public education centre. We collected gas and electricity bills and recorded indoor temperatures for over a year to determine heating and cooling set points and the schedules. Our analysis showed that 47.42% of the electricity bills were unused. To reduce energy demand and improve thermal comfort, we both developed and validated a Building Energy Modelling (BEM) approach using TRNSYS18 and weather data during year 2021. The BEM model allowed us to propose efficient measures to meet the Standard Passive requirements. Our results demonstrate the effectiveness of energy audits and BEM modelling in reducing energy consumption in public education buildings.

Impacts of COVID-19 on educational buildings energy consumption: case study of the university of Jordan

The global lockdowns adopted by many countries to combat the outbreak of the COVID-19 pandemic led to a significant transformation in the teaching methods adopted in higher education institutions toward dependence on online learning systems. Although this pandemic has placed a technical and financial burden on academic institutions to facilitate the successful transition to online learning, it provides opportunities to understand the impacts of adopting new policies and strategies to improve the efficient utilization of resources and thus reduce operational costs. The detailed analyses of the changes in energy consumption can support assessing the potential savings in electricity bills with the wide-scale adoption of online learning methods in the future as a business as usual to improve and modernize the education systems. This paper provides a detailed analysis of the electrical energy consumption of the buildings within the campus of the University of Jordan. The diversity of building types on the campus (e.g., university hospital, humanities schools, scientific schools) supports the provision of more general and robust recommendations to extend the results to other institutions, particularly in developing countries. The Energy Use Intensity (EUI) per unit area and EUI per student are employed for the first time for benchmarking the energy usage in educational buildings in Jordan. Overall, the analyses show that the total electricity consumption in 2020 was significantly lower than in 2019, with a decrease of 20.8% from 27.7 GWh in 2019 to 21.9 GWh in 2020. It is also found that the most significant reduction occurred in the humanities buildings (i.e., a 39% drop in energy consumption). However, this volume of energy reduction is still relatively low, considering the absence of students. Furthermore, the hospital has an extremely high EUI value (161 kWh/m2/year) compared to the other categories (e.g., the EUI for humanities schools is 32.5 kWh//m2/year). To conclude, the electrical energy consumption data suggests that there may be significant opportunities for energy conservation in all building categories, especially in the hospital.

A Modern Strategy for the Selection of Efficacious Solar Panels at Industrial Level under an Interval-Valued Picture Fuzzy Set Hybrid Environment

Solar panels hold a significant amount of importance at an industrial level because they contribute to environmental sustainability by reducing carbon emissions, contribute to cost savings through reduced electricity bills, provide energy independence and reliability, and serve as a long-term investment with attractive returns. One of the objectives of this research article is to address the limitations of the current score function utilized in multi-criteria decision-making (MCDM) problems within an interval-valued picture fuzzy (IVPF) environment and to present an enhanced score function. Moreover, IVPF hybrid arithmetic operators and IVPF hybrid geometric operators are introduced in this article. These operators are further thoroughly examined to identify their key characteristics. By conducting a case study, an algorithm is formulated to select the most suitable solar panel to maximize energy availability at the industrial level in the framework of the newly proposed IVPF hybrid operators. In addition, a comprehensive comparative study is conducted to demonstrate the significance and validity of recently proposed novel techniques compared with existing methods.

Determinants of Solar Energy Demand among Households in Maiduguri, Borno State Nigeria

The study assessed the prospect of solar energy demand in Maiduguri, Borno State Nigeria, using primary data obtained through snowball sampling method collected between June and July 2022. The study employed descriptive statistics, Ordinary Least Square, and logistic regressions to achieve its objectives. The result showed that 73% of the household heads are male, with 30% of them as businessmen. In addition, 80% are married; while 47% have a first degree. Most have income between N50, 000 and above. Most of them installed solar of N5, 00 000 to N 1, 000 000. The reasons for solar demand include poor electricity supply (32%), convenience (32%), comfortability (29%) and affordability (7%). About 70% of the respondents have been using solar for 2 to 4 years. Similarly, 75% and 80% reported that their electricity bills and generator maintenance cost have declined from N10, 000 and above to N5, 000 or less monthly after solar installation. The 95% of the respondents are satisfied with solar energy with the level of their Income, convenience and education on solar energy positively influencing their solar energy demand at 5%, 5%, and 10% respectively. In addition, the duration of solar power supply by the appliances, affordability of solar energy equipment as well as the comfort its provides coupled with the poor electricity supply in the study area positively and significantly influence solar energy demand at 5%, 5%, 10% and 5% respectively while reliability of the solar equipment negatively influences solar energy demand. Moreover, income is the most important determinant of solar energy demand with households that spent more on solar energy installation are more satisfied. Therefore, the study recommends policies that improve income, reduce the cost of solar installation and create more awareness of the advantages of solar power.

DETECTION OF ELECTRICITY THEFT CYBER-ATTACKS IN RENEWABLE DISTRIBUTED GENERATION FOR FUTURE IOT-BASED SMART ELECTRIC METERS

Electricity theft represents a pressing problem that has brought enormous financial losses to electric utility companies worldwide. In the United States alone, $6 billion worth of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption domain via physical attacks that includes line tapping or meter tampering. The smart grid paradigm opens the door to new forms of electricity theft attacks. First, electricity theft can be committed in a cyber manner. With the advanced metering infrastructure (AMI), smart meters are installed at the customers’ premises and regularly report the customers’ consumption for monitoring and billing purposes. In this context, malicious customers can launch cyber-attacks on the smart meters to manipulate the readings in a way that reduces their electricity bill. Second, the smart grid paradigm enables customers to install renewable-based distributed generation (DG) units at their premises to generate energy and sell it back to the grid operator and hence make a profit.
 Therefore, this project evaluating performance of various deep learning algorithms such as deep feed forward neural network (DNN), recurrent neural network with gated recurrent unit (RNN-GRU) and convolutional neural network (CNN) for electricity cyber-attack detection. Now-a-days in advance countries solar plates are used to generate electricity and these users can sale excess energy to other needy users and they will be maintained two different meters which will record consumption and production details. While producing some malicious users may tamper smart meter to get more bill which can be collected from electricity renewable distributed energy. This attack may cause huge losses to agencies. To detect such attack, this project is employing deep learning models which can detect all possible alterations to predict theft.

Heat pumps and thermal energy storages centralised management in a Renewable Energy Community.

This paper examines a Renewable Energy Community (REC) made up of 10 dwellings that collectively self-consume energy produced by a photovoltaic field connected to a water purifier. Each dwelling heat demand is satisfied by means of Heat Pump (HP) coupled with Thermal Energy Storage (TES), which can be managed to perform load shifting and increase collective-self-consumption (CSC). Techno-economic analyses are performed accounting for HPs' COP variation with temperature and part load operations, as well as TES heat dispersion. A new centralised control strategy for HPs is proposed and a sensitivity analysis is performed to assess the impact of varying TES system capacity. The results show that the centralised strategy can increase the CSC by 12-30%, with TES sizes of 100-1000 litres respectively. But the electricity consumption of HPs increases by 2-5% due to higher storage system temperatures causing worse average COPs by 2.3-0.6% and higher thermal losses by 29-58%. As a result, REC's energy independence rise, as does the amount of CSC incentives, but electricity bills also increase. Comparing these trends shows that CSC incentives should be adjusted according to energy prices to ensure cost-effective outcomes for all stakeholders and encourage the adoption of similar centralised control strategies.

A sodium-ion-conducted asymmetric electrolyzer to lower the operation voltage for direct seawater electrolysis

Hydrogen produced from neutral seawater electrolysis faces many challenges including high energy consumption, the corrosion/side reactions caused by Cl-, and the blockage of active sites by Ca2+/Mg2+ precipitates. Herein, we design a pH-asymmetric electrolyzer with a Na+ exchange membrane for direct seawater electrolysis, which can simultaneously prevent Cl- corrosion and Ca2+/Mg2+ precipitation and harvest the chemical potentials between the different electrolytes to reduce the required voltage. In-situ Raman spectroscopy and density functional theory calculations reveal that water dissociation can be promoted with a catalyst based on atomically dispersed Pt anchored to Ni-Fe-P nanowires with a reduced energy barrier (by 0.26 eV), thus accelerating the hydrogen evolution kinetics in seawater. Consequently, the asymmetric electrolyzer exhibits current densities of 10 mA cm−2 and 100 mA cm−2 at voltages of 1.31 V and 1.46 V, respectively. It can also reach 400 mA cm−2 at a low voltage of 1.66 V at 80 °C, corresponding to the electricity cost of US$1.36 per kg of H2 ($0.031/kW h for the electricity bill), lower than the United States Department of Energy 2025 target (US$1.4 per kg of H2).

A comparison of optimal peak clipping and load shifting energy storage dispatch control strategies for event-based demand response

Buildings, specifically large commercial buildings, are key contributors to the increasing electrical energy demand that is taxing the reliability of an ageing U.S. power grid. Through utility sponsored demand response programs and electrical energy storage systems, large buildings can simultaneously save money on their electricity bill and improve power grid reliability – with little to no change in their operations. Few studies have explored demand response benefits and appropriate control strategies for large commercial buildings. In this study, optimal peak clipping and load shifting control strategies of a Li-ion battery energy storage system are formulated and analyzed over 2 years of 15-minute interval demand data for a large commercial building in the Southwest United States. Furthermore, this analysis assesses the discounted payback period of a Li-ion battery energy storage system while considering cases with and without enrollment in the local utility’s event-based demand response program. Degradation in the Li-ion battery energy storage system’s rated power and capacity are considered throughout this analysis. Key findings in this study show that enrollment in event-based demand response can provide a reasonable (<10 years) discounted payback period of Li-ion battery energy storage systems.

Mplementation and Testing of a Practical Product to Balance Single-Phase Loads in a Three-Phase System at the Distribution and Unit Levels

The crucial worry in the production of electricity is to be certain that the quality of the supplied electricity meets requirements. Nowadays, and with the development of technology, there are new energy sources that can add stress to the network such as renewable energy. Being supplied with high electrical quality is a must. One of the basic methods that are recommended to be introduced to the networks is to deal with load balancing at low voltage. Load reconfiguration is one of the best solutions that can be applied to reach balance. The main focus of this paper is to find the optimal consumer load rearrangement at the installation level. These changes are made to maintain the balance between the phases, to reduce the number of electricity bills, and to reduce the fuel consumption that affects the environment negatively. Certain constraints and equations are needed to optimize load distribution. Out of these constraints, an algorithm was made followed by an appropriate switching process to do the automatic balancing. This technique guarantees better results. The proposed process was tested on real data of the different types of loads with different profiles and in every single case, accepted results were obtained. To sum up, the main goal of this paper is to achieve the balancing of single-phase loads in a three-phase system. To make sure of the theoretical results, implementation was done at the University of Balamand’s laboratories using resistive loads and the physical results were always close to the theoretical outcomes.

‘Just’ access to electricity: Energy justice in Indonesia’s rural electrification (LISDES) program

This study examined issues that emerge from Indonesia’s rural electrification (LISDES) program. A qualitative study was used as an approach. Document analysis and semi-structured interviews are the methods selected for data collection. Using energy justice as an analytical framework, three categories of energy injustices were identified in the program: distributive, procedural, and recognition injustice. Unequal income to pay electricity bills; uneven geographical conditions; and unequal distribution of the population were identified as the elements of distributive injustice. Inadequate access to informational disclosure on the problems and solutions of the program; lack of meaningful participation of subnational actors; and lack of a comprehensive law and regulatory framework in place to protect private sectors in the program were demonstrated as the elements of procedural injustice. Lastly, a lack of understanding of the various meanings of electricity as the key to public welfare; and non-recognition of distinctive socioeconomic characteristics in various locations of Indonesia were found as the key elements of recognition injustice. These findings suggest that to achieve 'just' access to electricity in the LISDES program, it is critical to address all elements of energy injustice identified in the program and clearly define long-term goals for the program.

ACCEPTANCE AND USAGE OF ENERGY-EFFICIENT SOLAR DEVICES BY THE RESIDENTS OF COIMBATORE CITY

The study investigated the usage of energy-efficient solar in regards to demographics of the residents. Data from 400 households in Coimbatore city (that installed minimum of one solar efficient device) was gathered bewtween 2019 and 2020. The magnitude of the energy-efficient solar device acceptability level by the households was calculated. The results revealed that respondents were open to use energy-efficient solar devices. However, devices were unable to be used as alternative sources because of limitations such as their high cost and households’ ignorance on the benefits that energy-efficient solar devices can offer. The study revealed that 45.3 percent of households agreed to use energy-efficient solar devices because they are environmentally safe, whereas, 31 percent of the households strongly agreed. The Households that agreed to utilize energy-efficient solar devices to lower their electricity bills were 49.8 percent and 34.8 percent strongly agreed for the same. Only 8.5 percent of households strongly agreed, while 40.5 percent strongly disagreed, that energy-efficient solar devices had extra benefits. Only 8.5 percent of the respondents strongly agreed that they use energy-efficient devices to get benefits from government subsidies, whereas, 32.3 percent strongly disagreed. Only 4.8 percent of the repsondents (out of 38%) strongly agreed that energy-efficient solar devices had diverse applications.

Analysis Of Output Power Control Of Mini On Grid Plts Without Kwh Exim In Fajrul Iman Patumbak Modern Islamic Boarding School

The decreasing of world’s supply of fossil energy caused by the human need for energy continues to increase. As a result, energy prices will be more expensive. Solar cells that utilize solar energy are a source of renewable electrical energy (RE). The use of solar cells to generate electricity can be used to reduce the burden of electricity bills anywhere as in Islamic boarding schools. The burden of electricity bills at the Fajrul Iman Patumbak Modern Islamic Boarding School, Deli Serdang Regency is quite a burden on Islamic boarding school expenses. To reduce the burden of spending on electricity bills at Islamic boarding schools, solar cells and control equipment are installed so that they can generate electricity. The control system is installed using an on-grid system, the electricity generated can be directly used by the Islamic boarding school during the day and will reduce some of the power absorbed/taken from PLN. After this service is carried out, the cost of spending on Islamic boarding schools has decreased.

Energy storage systems for commercial buildings in dense urban regions: NYC case study

In response to the mounting interest in Battery Energy Storage Systems (BESS) from a wide range of entities—commercial, private, and governmental—this paper analyzes the decision-making criteria for BESS implementation. The structure of electricity bills are analyzed to understand the current cost structure of building utility tariffs. In addition, the potential revenue streams provided by various incentive programs, including demand response contracts, when implementing BESS were investigated. Highlighting the need for a pragmatic and efficient ranking system, we propose a model that leverages load profile data to determine the suitability of BESS implementation within a portfolio of buildings. This approach offers property owners a streamlined method to prioritize which buildings to equip with BESS. To validate the efficacy of our load profile-based model, we compare it against established financial models, specifically employing the System Advisory Model (SAM) from the National Renewable Energy Lab (NREL). Our findings indicate that a load profile-centric ranking system can effectively determine the most appropriate buildings for ESS implementation. This paper provides a substantial contribution to the field of BESS sizing, offering a simplified yet robust methodology for ranking that aligns well with more complex financial models. By focusing on the practical utility of load profile data, we enable a simplified and informed process for property managers to make sound decisions in the deployment of energy storage systems.